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Don’t settle: 4 keys to finding the right data science role

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By: Lyndsey Padden, VP, Data Science
Pexels Ketut Subiyanto 4474047

Welcome to the fifth 84.51° Data University blog, a series of quarterly insights for prospective and current data science professionals.

If you are in the market for a role in data science—whether it be entry or more senior—what key indicators should you look for to ensure you'll find not only a job, but a career? As a data science talent strategy leader and former jobseeker, I’ve seen the answer to this question evolve in recent years. While I can't share all of the secret sauce behind what makes data science at 84.51° a standout, I can share my perspective on what I believe solid organizations will have in place to foster an engaged and developing data science community.

Demand for data scientists

As I started my own job search on the tail end of a global recession, I was eager to land a role at an organization where I could marry my passion for consumer behavior analysis with a means of paying off student loan debt. Back then, before Harvard Business Review (HBR) had declared data science to be the sexiest job of the 21st century, the landscape of data science and data-led organizations was fairly straightforward. Over a decade later, I've been fortunate to grow a career in data science at an organization with values that align with my own, people I learn from daily, and work I find challenging.

To anyone navigating today's job market, that sounds simple enough, right? Find a data science role at a company with a solid culture, learning opportunities, and cool work—Done! For better AND worse, today's hiring climate is far more complex with opportunities spanning industries and across organizations in various places on the data maturity curve. What appears to be a great opportunity might not be the right fit.

According to data sourced from Glassdoor, data science role postings grew 480% between 2017 and 2022. Today a variety of organizations across diverse domains tout budding data science teams and promises of a data-driven decisioning. While we are over a decade beyond 2012, the U.S. Bureau of Labor Statistics projects employment of data scientists to grow 36% between 2021 and 2031. The bottom line: the complexity of organizations and roles that play in the data science space has and will continue to grow. So, what should data scientists look for to increase their chances of joining a company where they can thrive? Start with these four criteria:

1. Commitment to talent development

Acquiring data science talent is truly just the start—the best organizations will be thinking beyond the hire and towards long-term development.

At 84.51° we recognize that development is needed not just for talent early in their career, but also for experienced talent looking to keep pace with an ever-changing field. We champion the 70/20/10 model of learning and apply that through skills-based learning and roles. 70/20/10 refers to holistic learning, with 10% coming from Education—think coursework, learning modules, etc.; 20% through Exposure—think mentoring relationships and seeing concepts learned applied by others; and 70% through Experience—getting hands-on opportunities to apply concepts that you have learned and seen yourself.

For early career talent, we have a structured program geared toward building a solid foundation before moving into a role in our business. This three-prong program involves a blend of live and asynchronous trainings (10% Educational learning), a simulated end-to-end case study (20% Exposure), and a live project experience featuring active work in our business (70% Experience). This program helps our talent establish baseline skills while providing an instant network and community of collaborators. While the program looked a lot different when I started with the company, I am a proud Early Career Program graduate myself and maintain relationships with others from my cohort today.

On the experienced talent side, we have a dedicated role in the function that is accountable for building a strategy to serve up clear, consistent content and opportunities across the 70/20/10 model for data scientists. This comes to life through robust and evolving skills taxonomies that connect skills to work and roles via a learning experience platform. Time to learn can be a huge barrier to prioritizing learning, so we strive to integrate that into our culture through things like structured hack days, weekly data science community share-and-learn sessions, and monthly org-wide meeting-free time to make space for learning.

2. Community of collaborators, learners and problem solvers

In addition to a culture where skills-based learning has a strategy, seek out companies that have an established community and well-articulated roles.

At 84.51° we have nearly 300 people in roles in the Data Science and Research job family that support data science, advanced analytics, and research projects. These roles sit alongside other technology functions including Product & Design and Engineering, allowing for collaboration and career mobility. While no two roles are exactly the same, our data science function links skills to roles through role archetypes. Today we have 5 core archetypes in data science:

  • Analysts use data to uncover insights and patterns that tell stories to solve business problems. Our analysts may be experts in measurement, reporting, data assets, or data visualization.

  • Data Scientists use scientific methods to develop sciences to solve business problems. These roles pair backgrounds in mathematics and statistics with strong business acumen and experience working with large datasets and relational databases. Intermediate to advanced programming skills for model development are required.

  • Data Science Consultants work closely with clients and partners to scope and deliver data-driven recommendations and actionable insights. They are experts in leveraging our existing sciences and capabilities to solve customer problems and articulate their value. Talent in these roles consult with our Product, Engineering, and Platform teams to ensure science is integrated into our scaled products.

  • Machine Learning Engineers deploy and maintain computationally efficient ML implementations, frameworks, tools, and end-to-end solutions. This role requires a strong understanding of math, algorithms, ML, and data pipelines along with DevOps & MLOps best practices.

  • Research Scientists create, tailor, apply, and test solutions in new and emerging areas of advanced statistics and machine learning. The role involves strong programming skills coupled with extensive, specialized experience.

We have many people who have played across a variety of these roles as well as others who have chosen a more specialized path. Our early career and more junior roles are typically more generalist in nature while we start to see the above differentiation through career progression. We also offer a unique program—talent rotation—that enables mobility within the company but across different business team verticals and role archetypes. This enables people to try (and learn) new skills and domains with support from leadership to find the right opportunity.

3. Stimulating, diverse work

The umbrella that covers data science work can be big. In my time in this role, I have encountered a number of people who have landed a role at an outside organization only to realize that the work wasn't what they expected or that the opportunity to specialize in a skill and then scale it more broadly wasn’t there.

Data Science and Research isn’t just a job function in our organization—it is our organization, embedded into each of our business verticals serving Kroger, consumer packaged goods clients, agencies, and above all else, the customer. Put simply, we have work and roles than span applied methods and subject matter disciplines:

  • Applied Methods: Optimization, Forecasting, Causal Inference, Segmentations, Embeddings, Targeting Sciences, Machine Learning, etc.

  • Subject Matter Disciplines: Supply Chain, Marketing, Price & Promotion, Media, Personalization, Healthcare, etc.

At 84.51° there is opportunity to learn and apply new methods, scale a facet of science development across a new domain…and everything in between.

4. Do your research

When networking I am often asked: "What differentiates data science teams at 84.51° and how do you keep people engaged in such a tough market?" While the answer evolves based on the needs of our people and business, I anchor on the things I have highlighted above and the nearly 300 data scientists who keep our culture sacred. Development, community, and delivery of meaningful work start as leadership priorities, but truly come to life through the talent.

So how do you find this for yourself when sorting through a litany of options and roles in data science?

  • As you speak with recruiters and interview for roles, look for passion. Make sure you feel it authentically and that those you interact with do, too.

  • As you interview and are invited to bring your own questions, ask about development, talent base, and diversity of work. Are they bluffing or do you buy it? Consider how what is shared aligns with your personal career goals.

I recently met with an experienced hire new to 84.51° and I asked him a pretty standard ice breaker question: What brought to you 84.51°? I was expecting one of the generic answers that I am still happy to claim about the organization I know and love—the people, work-life balance, data-driven decisioning, etc. His actual response, while totally logical and sound, took me by surprise: He looked at the data. Our tenure rates, online reviews, and the comfort of a tech company backed by the stability of a Fortune 25 grocery retailer sold him.

My final parting advice? To all the aspiring and thriving data scientists out there looking to launch or grow rewarding careers, do what you do best—analyze the offerings. And to all the orgs looking to attract and retain top tier talent—foster environments that champion learning & development backed by a diverse portfolio of work that delivers real business value.

I see those values in action at 84.51° and invite any job seeker looking to learn more to check out our opportunities. Ready to join us? Check out Careers at 84.51° to learn more.

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Lyndsey Padden, VP, Data Science
As Vice President of Data Science, Lyndsey Padden oversees data science functional and talent strategies including talent acquisition, early career programs, learning and development, and standards. While Lyndsey’s ex...

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